Support Vector Classifier with a Fuzzy-Value Class Label

Yang, Chan-Yun

doi:10.1007/978-3-540-28647-9_84

Support Vector Classifier with a Fuzzy-Value Class Label

Chan-Yun Yang¹⁹

Conference paper

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5 Citations

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3173))

Abstract

The purpose of this paper is to introduce a concept of fuzzy class memberships to the samples of training set in the support vector classifier. The inclusion of fuzzy values contributed a set of dynamic Lagrangian constraints, which setups a more specific space for searching the optimum, and conducted a more accurate classification performance. The developed model stepped into the sub-structure of the classifier, and involved the complex micro-interactions among the training samples to form a more precise separating hyperplane by fuzzy membership. The micro-interactions also altered the hyperplane and its corresponding margin, and achieved the deep-reaching classification accuracy around the sub-optimal region.

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References

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Authors and Affiliations

Department of Mechanical Engineering, Kuang-Wu Institute of Technology, No. 151, I-Deh St., Peitou, Taipei, Taiwan, 112
Chan-Yun Yang

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Chan-Yun Yang
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Editors and Affiliations

School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, China
Fu-Liang Yin
Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
Jun Wang
School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, Liaoning, China
Chengan Guo

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Yang, CY. (2004). Support Vector Classifier with a Fuzzy-Value Class Label. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_84

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DOI: https://doi.org/10.1007/978-3-540-28647-9_84
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22841-7
Online ISBN: 978-3-540-28647-9
eBook Packages: Springer Book Archive

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